Evaluation of the Radiomics Method for the Prediction of Atypical Adenomatous Hyperplasia in Patients With Subcentimeter Pulmonary Ground-Glass Nodules

ObjectivesWe aimed to develop a prediction model to distinguish atypical adenomatous hyperplasia (AAH) from early lung adenocarcinomas in patients with subcentimeter pulmonary ground-glass nodules (GGNs), which may help avoid aggressive surgical resection for patients with AAH.MethodsSurgically confirmed cases of AAH and lung adenocarcinomas manifesting as GGNs of less than 1 cm were retrospectively collected. A prediction model based on radiomics and clinical features identified from a training set of cases was built to differentiate AAH from lung adenocarcinomas and tested on a validation set.ResultsFour hundred and eighty-five eligible cases were included and randomly assigned to the training (n = 339) or the validation sets (n = 146). The developed radiomics prediction model showed good discrimination performance to distinguish AAH from adenocarcinomas in both the training and the validation sets, with, respectively, 84.1% and 82.2% of accuracy, and AUCs of 0.899 (95% CI: 0.867–0.931) and 0.881 (95% CI: 0.827–0.936).ConclusionThe prediction model based on radiomics and clinical features can help differentiate AAH from adenocarcinomas manifesting as subcentimeter GGNs and may prevent aggressive resection for AAH patients, while reserving this treatment for adenocarcinomas.

Download Full-text

Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules

Scientific Reports ◽

10.1038/s41598-021-83167-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Wufei Chen ◽

Ming Li ◽

Dingbiao Mao ◽

Xiaojun Ge ◽

Jiaofeng Wang ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Predictive Performance ◽

Ground Glass ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Validation Set ◽

Lung Adenocarcinomas ◽

Radiomics Signature ◽

Ground Glass Nodules

AbstractControversy and challenges remain regarding the cognition of lung adenocarcinomas presented as subcentimeter ground glass nodules (GGNs). Postoperative lymphatic involvement or intrapulmonary metastasis is found in approximately 15% to 20% of these cases. This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma appearing as subcentimeter ground glass nodules. We retrospectively enrolled 318 subcentimeter GGNs with histopathology-confirmed adenocarcinomas in situ (AIS), minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC). The radiomics features were extracted from manual segmentation based on contrast-enhanced CT (CECT) and non-contrast enhanced CT (NCECT) images after imaging preprocessing. The Lasso algorithm was applied to construct radiomics signatures. The predictive performance of radiomics models was evaluated by receiver operating characteristic (ROC) analysis. A radiographic-radiomics combined nomogram was developed to evaluate its clinical utility. The radiomics signature on CECT (AUC: 0.896 [95% CI 0.815–0.977]) performed better than the radiomics signature on NCECT data (AUC: 0.851[95% CI 0.712–0.989]) in the validation set. An individualized prediction nomogram was developed using radiomics model on CECT and radiographic model including type, shape and vascular change. The C index of the nomogram was 0.915 in the training set and 0.881 in the validation set, demonstrating good discrimination. Decision curve analysis (DCA) revealed that the proposed model was clinically useful. The radiomics signature built on CECT could provide additional benefit to promote the preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.

Download Full-text

Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules

Quantitative Imaging in Medicine and Surgery ◽

10.21037/qims-20-840 ◽

2021 ◽

Vol 11 (5) ◽

pp. 1710-1722

Author(s):

Rong Niu ◽

Xiaonan Shao ◽

Xiaoliang Shao ◽

Zhenxing Jiang ◽

Jianfeng Wang ◽

...

Keyword(s):

Computed Tomography ◽

Positron Emission Tomography ◽

Prediction Model ◽

Clinical Characteristics ◽

Emission Tomography ◽

Ground Glass ◽

Model Based ◽

Positron Emission ◽

Pet Ct ◽

Ground Glass Nodules

Download Full-text

Analysis of CT morphologic features and attenuation for differentiating among transient lesions, atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive and invasive adenocarcinoma presenting as pure ground-glass nodules

Scientific Reports ◽

10.1038/s41598-019-50989-1 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Lin Qi ◽

Ke Xue ◽

Cheng Li ◽

Wenjie He ◽

Dingbiao Mao ◽

...

Keyword(s):

Minimally Invasive ◽

Ground Glass ◽

Maximum Diameter ◽

Imaging Biomarkers ◽

Atypical Adenomatous Hyperplasia ◽

Adenomatous Hyperplasia ◽

Invasive Adenocarcinoma ◽

Ct Attenuation ◽

Ground Glass Nodules

Abstract Thin-section computed tomography (TSCT) imaging biomarkers are uncertain to distinguish progressive adenocarcinoma from benign lesions in pGGNs. The purpose of this study was to evaluate the usefulness of TSCT characteristics for differentiating among transient (TRA) lesions, atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) presenting as pure ground-glass nodules (pGGNs). Between January 2016 and January 2018, 255 pGGNs, including 64 TRA, 22 AAH, 37 AIS, 108 MIA and 24 IAC cases, were reviewed on TSCT images. Differences in TSCT characteristics were compared among these five subtypes of pGGNs. Logistic analysis was performed to identify significant factors for predicting MIA and IAC. Progressive pGGNs were more likely to be round or oval in shape, with clear margins, air bronchograms, vascular and pleural changes, creep growth, and bubble-like lucency than were non-progressive pGGNs. The optimal cut-off values of the maximum diameter for differentiating non-progressive from progressive pGGNs and IAC from non-IAC were 6.5 mm and 11.5 mm, respectively. For the prediction of IAC vs. non-IAC and non-progressive vs. progressive adenocarcinoma, the areas under the receiver operating characteristics curves were 0.865 and 0.783 for maximum diameter and 0.784 and 0.722 for maximum CT attenuation, respectively. The optimal cut-off values of maximum CT attenuation were −532 HU and −574 HU for differentiating non-progressive from progressive pGGNs and IAC from non-IAC, respectively. Maximum diameter, maximum attenuation and morphological characteristics could help distinguish TRA lesions from MIA and IAC but not from AAH. So, CT morphologic characteristics, diameter and attenuation parameters are useful for differentiating among pGGNs of different subtypes.

Download Full-text